Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery

Singh, Minerva; Malhi, Yadvinder; Bhagwat, Shonil A.

doi:10.1080/01431161.2014.903441

Cited by 32 publications

(38 citation statements)

References 46 publications

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“…This makes the optical approach inappropriate to estimate the biomass due to its saturation at high biomass value (Morel, Fisher, and Malhi 2012;Singh, Malhi, and Bhagwat 2014b). However, in another study, a texture-based approach was implemented with the application of Fourier transform texture ordination (FOTO) with SPOT-5 data and managed to solve the saturation issue (Singh, Malhi, and Bhagwat 2014a). This included a combination of two techniques, i.e.…”

Section: Estimation Of Agb and Carbon Productionmentioning

confidence: 99%

A review of remote sensing applications for oil palm studies

Chong

Kanniah

Pohl

et al. 2017

Geo-spatial Information Science

130

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a faculty of Geoinformation and real estate, Universiti teknologi malaysia, Johor Bahru, malaysia; b centre for environmental sustainability and Water security (ipasa), research institute for sustainable environment (rise), Universiti teknologi malaysia, Johor Bahru, malaysia; c institute of computer science, University of osnabrueck, osnabrueck, Germany ABSTRACT Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean, sunflower, and rapeseed. The growth of the oil palm industry causes degradation to the environment, especially when the expansion of plantations goes uncontrolled. Remote sensing is a useful tool to monitor the development of oil palm plantations. In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability, this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring. In addition, the existing knowledge gaps are identified and recommendations for further research are given.

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Section: Estimation Of Agb and Carbon Productionmentioning

confidence: 99%

A review of remote sensing applications for oil palm studies

Chong

Kanniah

Pohl

et al. 2017

Geo-spatial Information Science

130

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“…This method uses a combination of Fourier transforms and ordination approaches (such as principal component analysis (PCA)) to quantify canopy structure and AGB heterogeneity and how this influences AGB variations [10]. This method has been used for quantifying AGB stock variability in varied tropical forested ecosystems, such as coastal mangrove forests [14] and natural and plantation forests [10,15]. Wavelet-derived texture methods are also increasingly used to characterize different forest types, particularly their areal delineation.…”

Section: Introductionmentioning

confidence: 99%

Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth

Singh

Evans²,

Friess

et al. 2015

Remote Sensing

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This study develops a modelling framework for utilizing very high-resolution (VHR) aerial imagery for monitoring stocks of above-ground biomass (AGB) in a tropical forest in Southeast Asia. Three different texture-based methods (grey level co-occurrence metric (GLCM), Gabor wavelets and Fourier-based textural ordination (FOTO)) were used in conjunction with two different machine learning (ML)-based regression techniques (support vector regression (SVR) and random forest (RF) regression). These methods were implemented on both 50-cm resolution Digital Globe data extracted from Google Earth™ (GE) and 8-cm commercially obtained VHR imagery. This study further examines the role of forest biophysical parameters, such as ground-measured canopy cover and vertical canopy height, in explaining AGB distribution. Three models were developed using: (i) horizontal canopy variables (i.e., canopy cover and texture variables) plus vertical canopy height; (ii) horizontal variables only; and (iii) texture variables only. AGB was variable across the site, ranging from 51.02 Mg/ha to 356.34 Mg/ha. GE-based AGB estimates were comparable to OPEN ACCESS Remote Sens. 2015, 7 5058 those derived from commercial aerial imagery. The findings demonstrate that novel use of this array of texture-based techniques with GE imagery can help promote the wider use of freely available imagery for low-cost, fine-resolution monitoring of forests parameters at the landscape scale.

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“…The r indicates the number of times that a pattern, here referring to the spatial structure of the forest canopy, reproduces itself within the given window. Therefore, an image with a coarse texture will yield a radial spectrum which is skewed toward low frequencies, while fine texture is expected to generate more balanced spectra [18]. FOTO indices are very sensitive to canopy structure; some situations such as gaps, fallen trees and the natural decaying of trees will cause bias in the computation of r-spectra.…”

Section: Foto Methodsmentioning

confidence: 99%

“…Proisy et al [14] implemented FOTO for coastal mangrove forests in French Guiana using IKONOS data; the results showed that saturation phenomena did not appear even for high biomass levels greater than 450 t/ha. Singh et al [18] indicated that the FOTO approach proved powerful in distinguishing different forest types and in developing individual biomass estimate models for various forest types in Malaysian Borneo. FOTO texture indices also demonstrated a strong correlation with forest AGB in Central Africa for R 2 = 0.85; residual standard error (RSE) = 15% [11].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have indicated that FOTO texture indices could be regarded as good predictors of AGB in tropical forests. The FOTO method has been applied to VHSR remote sensing data to derive forest biomass models in French Guiana [8,[14][15][16], Malaysian Borneo [17,18], Brazil Amazonia [8], and the Western Ghats of India [19]. Theoretically, optical remote sensing technologies have a limited capability of predicting forest biomass, particularly for those mature forests which spectral responses become saturated [5,7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Aboveground Biomass using Texture Indices from Aerial Photos in a Temperate Forest of Northeastern China

et al. 2016

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Optical remote sensing data have been considered to display signal saturation phenomena in regions of high aboveground biomass (AGB) and multi-storied forest canopies. However, some recent studies using texture indices derived from optical remote sensing data via the Fourier-based textural ordination (FOTO) approach have provided promising results without saturation problems for some tropical forests, which tend to underestimate AGB predictions. This study was applied to the temperate mixed forest of the Liangshui National Nature Reserve in Northeastern China and demonstrated the capability of FOTO texture indices to obtain a higher prediction quality of forest AGB. Based on high spatial resolution aerial photos (1.0 m spatial resolution) acquired in September 2009, the relationship between FOTO texture indices and field-derived biomass measurements was calibrated using a support vector regression (SVR) algorithm. Ten-fold cross-validation was used to construct a robust prediction model, which avoided the over-fitting problem. By further comparison the performance of the model estimates for greater coverage, the predicted results were compared with a reference biomass map derived from LiDAR metrics. This study showed that the FOTO indices accounted for 88.3% of the variance in ground-based AGB; the root mean square error (RMSE) was 34.35 t/ha, and RMSE normalized by the mean value of the estimates was 22.31%. This novel texture-based method has great potential for forest AGB estimation in other temperate regions.

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Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery

Cited by 32 publications

References 46 publications

A review of remote sensing applications for oil palm studies

A review of remote sensing applications for oil palm studies

Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth

Mapping Aboveground Biomass using Texture Indices from Aerial Photos in a Temperate Forest of Northeastern China

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